In recent years, generative AI has undergone major advancements, demonstrating significant promise in augmenting human productivity. Notably, large language models (LLM), with ChatGPT-4 as an example, have drawn considerable attention. Numerous articles have examined the impact of LLM-based tools on human productivity in lab settings and designed tasks or in observational studies. Despite recent advances, field experiments applying LLM-based tools in realistic settings are limited. This paper presents the findings of a field randomized controlled trial assessing the effectiveness of LLM-based tools in providing unmonitored support services for information retrieval.

Check out the on-demand sessions from the Low-Code/No-Code Summit to learn how to successfully innovate and achieve efficiency by upskilling and scaling citizen developers. We love stories of dramatic breakthroughs and neat endings: The lone inventor cracks the technical challenge, saves the day, the end. These are the recurring tropes surrounding new technologies. Unfortunately, these tropes can be misleading when we're actually in the middle of a technology revolution. It's the prototypes that get too much attention rather than the complex, incremental refinement that truly delivers a breakthrough solution.